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""" |
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Processor class for Llava. |
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Modified to include support for Gemma tokenizer. |
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""" |
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from typing import List, Optional, Union |
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from transformers.feature_extraction_utils import BatchFeature |
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from transformers.image_utils import ImageInput |
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from transformers.processing_utils import ProcessorMixin |
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from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy |
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from transformers.utils import TensorType |
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class LlavaGemmaProcessor(ProcessorMixin): |
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r""" |
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Constructs a Llava processor which wraps a Llava image processor and a Llava tokenizer into a single processor. |
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[`LlavaProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the |
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[`~LlavaProcessor.__call__`] and [`~LlavaProcessor.decode`] for more information. |
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Args: |
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image_processor ([`CLIPImageProcessor`], *optional*): |
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The image processor is a required input. |
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tokenizer ([`LlamaTokenizerFast`], *optional*): |
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The tokenizer is a required input. |
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""" |
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attributes = ["image_processor", "tokenizer"] |
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image_processor_class = "CLIPImageProcessor" |
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tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast", |
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"GemmaTokenizer", "GemmaTokenizerFast") |
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def __init__(self, image_processor=None, tokenizer=None): |
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super().__init__(image_processor, tokenizer) |
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def __call__( |
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self, |
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text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, |
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images: ImageInput = None, |
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padding: Union[bool, str, PaddingStrategy] = False, |
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truncation: Union[bool, str, TruncationStrategy] = None, |
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max_length=None, |
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return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH, |
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) -> BatchFeature: |
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""" |
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Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` |
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and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode |
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the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to |
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CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring |
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of the above two methods for more information. |
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Args: |
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text (`str`, `List[str]`, `List[List[str]]`): |
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The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings |
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(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set |
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`is_split_into_words=True` (to lift the ambiguity with a batch of sequences). |
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images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): |
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The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch |
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tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a |
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number of channels, H and W are image height and width. |
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padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): |
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Select a strategy to pad the returned sequences (according to the model's padding side and padding |
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index) among: |
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- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single |
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sequence if provided). |
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- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum |
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acceptable input length for the model if that argument is not provided. |
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- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different |
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lengths). |
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max_length (`int`, *optional*): |
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Maximum length of the returned list and optionally padding length (see above). |
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truncation (`bool`, *optional*): |
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Activates truncation to cut input sequences longer than `max_length` to `max_length`. |
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return_tensors (`str` or [`~utils.TensorType`], *optional*): |
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If set, will return tensors of a particular framework. Acceptable values are: |
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- `'tf'`: Return TensorFlow `tf.constant` objects. |
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- `'pt'`: Return PyTorch `torch.Tensor` objects. |
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- `'np'`: Return NumPy `np.ndarray` objects. |
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- `'jax'`: Return JAX `jnp.ndarray` objects. |
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Returns: |
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[`BatchFeature`]: A [`BatchFeature`] with the following fields: |
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- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. |
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- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when |
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`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not |
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`None`). |
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- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. |
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""" |
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if images is not None: |
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pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"] |
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else: |
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pixel_values = None |
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text_inputs = self.tokenizer( |
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text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length |
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) |
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return BatchFeature(data={**text_inputs, "pixel_values": pixel_values}) |
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def batch_decode(self, *args, **kwargs): |
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""" |
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This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please |
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refer to the docstring of this method for more information. |
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""" |
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return self.tokenizer.batch_decode(*args, **kwargs) |
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def decode(self, *args, **kwargs): |
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""" |
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This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to |
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the docstring of this method for more information. |
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""" |
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return self.tokenizer.decode(*args, **kwargs) |
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@property |
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def model_input_names(self): |
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tokenizer_input_names = self.tokenizer.model_input_names |
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image_processor_input_names = self.image_processor.model_input_names |
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return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) |
